In the heart of China’s energy transition, a groundbreaking study led by Hai Jiang of the China Renewable Energy Engineering Institute is shedding light on the untapped potential of rural renewable resources. Published in the journal *Energies*, the research combines cutting-edge technology and innovative modeling to assess solar, wind, and biomass energy resources in pilot cities across the country.
Jiang and his team employed a trio of advanced methods to evaluate these resources. Convolutional neural network (CNN) technology rapidly identified available roof areas for solar panels, while Greenwich engineering software simulated wind resources with remarkable local terrain adaptability. Statistical yearbook methods rounded out the assessment, providing a comprehensive picture of rural energy potential.
The results are promising. The study found that photovoltaic power generation capacity could reach approximately 15.63 GW, with wind power potential at 458.3 MW. Additionally, agricultural waste could be converted into an equivalent of 433,900 tons of standard coal. “The cities we studied are rich in wind, solar, and biomass resources,” Jiang noted, highlighting the vast untapped potential in rural areas.
But the research doesn’t stop at resource assessment. The team also optimized a hybrid power generation system using genetic algorithms, combining wind, solar, biomass, and coal power to balance annual electricity demand in rural areas. This integrated approach could pave the way for more stable and reliable energy systems in the future.
Looking ahead, the study used the Long-range Energy Alternatives Planning (LEAP) model to predict energy trends under different demand growth rates. In the clean coal scenario with carbon capture (WSBC-CCS), renewable energy and clean coal power are expected to dominate by 2030. Carbon dioxide emissions are projected to peak in 2024 and return to 2020 levels between 2028 and 2029. Under the pure renewable energy scenario (H_WSB), sulfur dioxide and nitrogen oxides emissions could be reduced by 23–25%, with carbon dioxide emissions approaching zero.
The implications for the energy sector are significant. As Hai Jiang explained, “This study evaluates the renewable energy potential, power system capacity optimization, and carbon emission characteristics of pilot cities at a macro scale.” The findings could guide future investments and policy decisions, shaping the trajectory of China’s energy transition.
Moreover, the research underscores the importance of data sensitivity analysis in future work. Understanding how variations in data can impact assessment results will be crucial for refining these models and ensuring their accuracy.
Published in the open-access journal *Energies*, this study offers a valuable resource for energy professionals, policymakers, and researchers alike. As the world grapples with the challenges of climate change and energy security, innovative research like this provides a beacon of hope and a roadmap for a sustainable future.